The cloud runtime environment is dynamic; therefore, allocating tasks to computing resources might include various scenarios. Metaheuristic algorithms are usually used to choose appropriate scheduling scenarios; however, they suffer from premature convergence, trapping in local optima, and imbalance between the exploration and exploitation of search space. The multi-verse optimizer (MVO) algorithm also suffers from similar problems. In this research, both Gabor filter and opposition-based learning methods are applied in the MVO algorithm to present the new algorithm GOMVO. The multi-tracker optimization (MTO) is applied in the GOMVO to present the new MTO-GOMVO hybrid algorithm. Then the scheduling framework MTOA-GOMVO@DSF is presented that applies the MTO-GOMVO metaheuristic algorithms in cloud computing scheduling. In the sequel, at first, the GOMVO algorithm is benchmarked applying CEC2017 benchmark functions and compared with several baseline algorithms in terms of mean error. Second, MTOA-GOMVO is also evaluated against related baseline algorithms in terms of mean error. Finally, MTOA-GOMVO is also applied in cloud computing to schedule independent tasks to virtual machines to improve average execution time, response time, throughput, and SLA violations. Simulation results applying NASA-iPSC real dataset showed that MTOA-GOMVO outweighs the baseline metaheuristic algorithms and performs well in scheduling cloud computing tasks.

云运行时环境是动态的；因此，将任务分配给计算资源可能包括各种场景。通常使用元启发式算法来选择合适的调度场景；然而，它们存在过早收敛、陷入局部最优以及搜索空间的探索和利用之间的不平衡问题。多节优化器 (MVO) 算法也存在类似问题。本研究将 Gabor 滤波器和基于对立的学习方法应用到 MVO 算法中，提出了新的 GOMVO 算法。在GOMVO中应用多跟踪器优化（MTO），呈现新的MTO-GOMVO混合算法。然后提出了将MTO-GOMVO元启发式算法应用于云计算调度的调度框架MTOA-GOMVO@DSF。在续集中，起初，GOMVO 算法使用 CEC2017 基准函数进行基准测试，并在平均误差方面与几种基线算法进行了比较。其次，MTOA-GOMVO 还根据相关的基线算法在平均误差方面进行了评估。最后，MTOA-GOMVO 还应用于云计算，将独立的任务调度到虚拟机，以提高平均执行时间、响应时间、吞吐量和 SLA 违规。应用 NASA-iPSC 真实数据集的仿真结果表明，MTOA-GOMVO 优于基线元启发式算法，并且在调度云计算任务方面表现良好。MTOA-GOMVO 还应用于云计算，将独立的任务调度到虚拟机，以提高平均执行时间、响应时间、吞吐量和 SLA 违规。应用 NASA-iPSC 真实数据集的仿真结果表明，MTOA-GOMVO 优于基线元启发式算法，并且在调度云计算任务方面表现良好。MTOA-GOMVO 还应用于云计算，将独立的任务调度到虚拟机，以提高平均执行时间、响应时间、吞吐量和 SLA 违规。应用 NASA-iPSC 真实数据集的仿真结果表明，MTOA-GOMVO 优于基线元启发式算法，并且在调度云计算任务方面表现良好。